The Tibetan Plateau (TP), the highest and largest plateau in the world, with complex and competing cryospheric‐hydrologic‐geodynamic processes, is particularly sensitive to anthropogenic warming. The quantitative water mass budget in the TP is poorly known. Here we examine annual changes in lake area, level, and volume during 1970s–2015. We find that a complex pattern of lake volume changes during 1970s–2015: a slight decrease of −2.78 Gt yr−1 during 1970s–1995, followed by a rapid increase of 12.53 Gt yr−1 during 1996–2010, and then a recent deceleration (1.46 Gt yr−1) during 2011–2015. We then estimated the recent water mass budget for the Inner TP, 2003–2009, including changes in terrestrial water storage, lake volume, glacier mass, snow water equivalent (SWE), soil moisture, and permafrost. The dominant components of water mass budget, namely, changes in lake volume (7.72 ± 0.63 Gt yr−1) and groundwater storage (5.01 ± 1.59 Gt yr−1), increased at similar rates. We find that increased net precipitation contributes the majority of water supply (74%) for the lake volume increase, followed by glacier mass loss (13%), and ground ice melt due to permafrost degradation (12%). Other term such as SWE (1%) makes a relatively small contribution. These results suggest that the hydrologic cycle in the TP has intensified remarkably during recent decades.

Water resources management, forecasting, and decision making require reliable estimates of precipitation. Extreme precipitation events are of particular importance because of their severe impact on the economy, the environment, and the society. In recent years, the emergence of various satellite‐retrieved precipitation products with high spatial resolutions and global coverage have resulted in new sources of uninterrupted precipitation estimates. However, satellite‐based estimates are not well integrated into operational and decision‐making applications because of a lack of information regarding the associated uncertainties and reliability of these products. In this study, four satellite‐derived precipitation products (CMORPH, PERSIANN, TMPA‐RT, and TMPA‐V6) are evaluated with respect to their performance in capturing precipitation extremes. The Stage IV (radar‐based, gauge‐adjusted) precipitation estimates are used as reference data. The results show that with respect to the probability of detecting extremes and the volume of correctly identified precipitation, CMORPH and PERSIANN data sets lead to better estimates. However, their false alarm ratio and volume are higher than those of TMPA‐RT and TMPA‐V6. Overall, no single precipitation product can be considered ideal for detecting extreme events. In fact, all precipitation products tend to miss a significant volume of rainfall. With respect to verification metrics used in this study, the performance of all satellite products tended to worsen as the choice of extreme precipitation threshold increased. The analyses suggest that extensive efforts are necessary to develop algorithms that can capture extremes more reliably.

Since the past three decades a great deal of effort is devoted to development of satellite-based precipitation retrieval algorithms. More recently, several satellite-based precipitation products have emerged that provide uninterrupted precipitation time series with quasi-global coverage. These satellite-based precipitation products provide an unprecedented opportunity for hydrometeorological applications and climate studies. Although growing, the application of satellite data for hydrological applications is still very limited. In this study, the effectiveness of using satellite-based precipitation products for streamflow simulation at catchment scale is evaluated. Five satellite-based precipitation products (TMPA-RT, TMPA-V6, CMORPH, PERSIANN, and PERSIANN-adj) are used as forcing data for streamflow simulations at 6-h and monthly time scales during the period of 2003-2008. SACramento Soil Moisture Accounting (SAC-SMA) model is used for streamflow simulation over the mid-size Illinois River basin. The results show that by employing the satellite-based precipitation forcing the general streamflow pattern is well captured at both 6-h and monthly time scales. However, satellites products, with no bias-adjustment being employed, significantly overestimate both precipitation inputs and simulated streamflows over warm months (spring and summer months). For cold season, on the other hand, the unadjusted precipitation products result in underestimation of streamflow forecast. It was found that bias-adjustment of precipitation is critical and can yield to substantial improvement in capturing both streamflow pattern and magnitude. The results suggest that along with efforts to improve satellite-based precipitation estimation techniques, it is important to develop more effective near real-time precipitation bias adjustment techniques for hydrologic applications.

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[1] Much of our knowledge about oceanic rainfall comes from spaceborne sensors. These sensors provide direct or indirect information used for precipitation retrievals through various algorithms. A thorough understanding of rain frequency and intensity and its regional distribution, which is especially important in a warming climate, requires an evaluation of the performance of rain-measuring sensors and identification of strengths and limitations offered by each sensor. The Tropical Rainfall Measuring Mission (TRMM) has enabled significant advancement in quantification of moderate to intense rainfall. However, a common limitation of the current suite of rain-measuring sensors is their lack of sensitivity to light rainfall, especially over subtropical and high-latitude oceans. Among various spaceborne sensors, CloudSat enables superior retrieval of light rainfall and drizzle. By using 3 years (2007)(2008)(2009)) of rainfall data from CloudSat and the precipitation radar aboard TRMM, it was determined that the quasi-global (60 S-60 N) oceanic mean rain rate is about 3.05 mm/d, considerably larger than that obtained from any individual sensor product. In the deep tropics, especially within 20 S-20 N, the sensors show the highest agreement, with a large fraction of total rain volume captured by the majority of sensors. However, toward higher latitudes and within the subtropical high-pressure regions, a significant fraction of rainfall, which can exceed 50% or more of total rain volume, is missed by the majority of the sensors.Citation: Behrangi, A., M. Lebsock, S. Wong, and B. Lambrigtsen (2012), On the quantification of oceanic rainfall using spaceborne sensors,

An intercomparison of high-latitude precipitation characteristics from observation-based and reanalysis products is performed. In particular the precipitation products from CloudSat provide an independent assessment to other widely used products, these being the observationally-based GPCP, GPCC and CMAP products and the ERA-Interim, MERRA and NCEP-DOE R2 reanalyses. Seasonal and annual total precipitation in both hemispheres poleward of 55° latitude is considered in all products, and CloudSat is used to assess intensity and frequency of precipitation occurrence by phase, defined as rain, snow or mixed phase. Furthermore, an independent estimate of snow accumulation during the cold season was calculated from the Gravity Recovery and Climate Experiment (GRACE). The intercomparison is performed for the 2007-2010 period when CloudSat was fully operational. It is found that ERA- Interim and MERRA are broadly similar, agreeing more closely with CloudSat over oceans. ERA-Interim also agrees well with CloudSat estimates of snowfall over Antarctica where total snowfall from GPCP and CloudSat is almost identical. A number of disagreements on regional or seasonal scales are identified: CMAP reports much lower ocean precipitation relative to other products, NCEP-DOE R2 reports much higher summer precipitation over northern hemisphere land, GPCP reports much higher snowfall over Eurasia, and CloudSat overestimates precipitation over Greenland, likely due to mischaracterization of rain and mixed-phase precipitation. These outliers are likely unrealistic for these specific regions and time periods. These estimates from observations and reanalyses provide useful insights for diagnostic assessment of precipitation products in high latitudes, quantifying the current uncertainties, improving the products, and establishing a benchmark for assessment of climate models.

This study contributes to characterization of satellite precipitation error which is fundamental to develop uncertainty models and bias reduction algorithms. Systematic and random error components of several satellite precipitation products are investigated over different seasons, thresholds and temporal accumulations. The analyses show that the spatial distribution of systematic error has similar patterns for all precipitation products. However, the systematic (random) error of daily accumulations is significantly less (more) than that of high resolution 3‐hr data. One should note that the systematic biases of satellite precipitation are distinctively different in the summer and winter. The systematic (random) error is remarkably higher (lower) during the winter. Furthermore, the systematic error seems to be proportional to the rain rate magnitude. The findings of this study highlight that bias removal methods should take into account the spatiotemporal characteristics of error as well as the proportionality of error to the magnitude of rain rate.

We show a recent increasing trend in Vapor Pressure Deficit (VPD) over tropical South America in dry months with values well beyond the range of trends due to natural variability of the climate system defined in both the undisturbed Preindustrial climate and the climate over 850–1850 perturbed with natural external forcing. This trend is systematic in the southeast Amazon but driven by episodic droughts (2005, 2010, 2015) in the northwest, with the highest recoded VPD since 1979 for the 2015 drought. The univariant detection analysis shows that the observed increase in VPD cannot be explained by greenhouse-gas-induced (GHG) radiative warming alone. The bivariate attribution analysis demonstrates that forcing by elevated GHG levels and biomass burning aerosols are attributed as key causes for the observed VPD increase. We further show that There is a negative trend in evaporative fraction in the southeast Amazon, where lack of atmospheric moisture, reduced precipitation together with higher incoming solar radiation (~7% decade−1 cloud-cover reduction) influences the partitioning of surface energy fluxes towards less evapotranspiration. The VPD increase combined with the decrease in evaporative fraction are the first indications of positive climate feedback mechanisms, which we show that will continue and intensify in the course of unfolding anthropogenic climate change.